20 CHAPTER 3 REVIEW OF CONJOINT ANALYSIS THEORY ...

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Data Collection The next step in CA involves the method of data collection. The issues of data collection for this study were the questionnaire design, the selection of a technique for survey administration, and the method of presentation for conjoint data collection. The aim is to present to respondents various attribute combinations i.e., product profiles that facilitate effective preference evaluation. Presentations can either be in a written or pictorial format. There are three main presentation methods. They include the trade-off, full-profile and pair-wise comparison methods. These methods will be discussed in the next section of this study. Methodology This section outlines the steps used to evaluate consumer attitudes concerning agricultural biotechnology and labeling formats for food products made using biotechnology. Steps of CA include: 1) attribute selection, 2) experimental design, 3) Questionnaire/survey construction, and 4) selection of an empirical model. Attribute selection plays an important role in CA because it affects the accuracy of the results and the relevance of the stimuli to real managerial decisions. Once the attribute and attribute levels have been selected, they must be combined into hypothetical products for respondents to rate or rank. This forms the basis for the experimental design, where respondents are shown various forms of a product and then asked to evaluate the different hypothetical products. In effect, the experimental design allows researchers to assess the effect of one or more independent variables (the attribute-level) on the dependent variable (product rating). To administer conjoint analysis, surveys are used as the data collection 25
method. The empirical model was designed based on the theory of complex decision making described in Louviere (1988, pages 12 -13). Attribute and Level Generation CA assumes that individuals evaluate product attributes in forming their preference for alternative products or services. It is critical to have a carefully thought out list of attributes. A list of too many attributes can greatly increase the burden on respondents since many attributes require evaluation of numerous product profiles. A list with too few attributes can reduce the predictive capabilities of the model because key pieces of information are missing from the model. The critical factor in specifying attributes and attribute levels is that a product cannot be accurately simulated if the product is not adequately defined. Attributes included in a conjoint study should be those most relevant to potential customers. An attribute is relevant to a product or conjoint survey if overlooking its existence leads to different predictions about the choice or ordering of the goods by the consumer. If the attribute does not positively or negatively influence a consumer’s preference function, it is considered irrelevant. (Lancaster, 1971). Attributes should represent a single concept, and be able to be used in the model so that any perceptual differences among individual are minimized. For example, an attribute such as quality should not be included and be specified by levels such as high, medium or low, because quality is relative and individuals will perceive quality differently. It is equally important when selecting attributes, to select their relevant levels, an important factor to consider because a balanced number of levels protects against attribute bias (Stringer, 1999). 26
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